| 1. |
- Al Sabbagh, Khaled, 1987, et al.
(författare)
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Improving Data Quality for Regression Test Selection by Reducing Annotation Noise
- 2020
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Ingår i: Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020. ; , s. 191-194
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Konferensbidrag (refereegranskat)abstract
- Big data and machine learning models have been increasingly used to support software engineering processes and practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the identification and reduction of noise that often comes in large data. In this paper, we present a noise reduction approach that deals with the problem of contradictory training entries. We empirically evaluate the effectiveness of the approach in the context of selective regression testing. For this purpose, we use a curated training set as input to a tree-based machine learning ensemble and compare the classification precision, recall, and f-score against a non-curated set. Our study shows that using the noise reduction approach on the training instances gives better results in prediction with an improvement of 37% on precision, 70% on recall, and 59% on f-score.
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| 2. |
- Penzenstadler, Birgit, 1981, et al.
(författare)
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Bots in Software Engineering
- 2022
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Ingår i: IEEE Software. - 1937-4194 .- 0740-7459. ; 39:5, s. 101-104
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Forskningsöversikt (refereegranskat)
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| 3. |
- Abrahao, Silvia, et al.
(författare)
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Open Source Software: Communities and Quality
- 2023
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Ingår i: IEEE Software. - 1937-4194 .- 0740-7459. ; 40:4, s. 96-99
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Tidskriftsartikel (refereegranskat)abstract
- This edition of the Practitioner's Digest features recent papers on open source software related to toxicity in open source discussions, newcomers in open source projects, quality of ansible scripts, code review practices, orphan vulnerabilities in open source software, and the relationship between community and design smells.
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| 4. |
- Antinyan, Vard, 1984, et al.
(författare)
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A Complexity Measure for Textual Requirements
- 2016
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Ingår i: Proceedings of 2016 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (Iwsm-Mensura). - : IEEE. - 9781509041473 - 9781509041480
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Konferensbidrag (refereegranskat)abstract
- Unequivocally understandable requirements are vital for software design process. However, in practice it is hard to achieve the desired level of understandability, because in large software products a substantial amount of requirements tend to have ambiguous or complex descriptions. Over time such requirements decelerate the development speed and increase the risk of late design modifications, therefore finding and improving them is an urgent task for software designers. Manual reviewing is one way of addressing the problem, but it is effort-intensive and critically slow for large products. Another way is using measurement, in which case one needs to design effective measures. In recent years there have been great endeavors in creating and validating measures for requirements understandability: most of the measures focused on ambiguous patterns. While ambiguity is one property that has major effect on understandability, there is also another important property, complexity, which also has major effect on understandability, but is relatively less investigated. In this paper we define a complexity measure for textual requirements through an action research project in a large software development organization. We also present its evaluation results in three large companies. The evaluation shows that there is a significant correlation between the measurement values and the manual assessment values of practitioners. We recommend this measure to be used with earlier created ambiguity measures as means for automated identification of complex specifications.
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| 5. |
- Antinyan, Vard, 1984, et al.
(författare)
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Identifying Complex Functions : By Investigating Various Aspects of Code Complexity
- 2015
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Ingår i: Proceedings of 2015 Science and Information Conference (SAI). - : IEEE Press. - 9781479985470 - 9781479985487 - 9781479985463 ; , s. 879-888
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Konferensbidrag (refereegranskat)abstract
- The complexity management of software code has become one of the major problems in software development industry. With growing complexity the maintenance effort of code increases. Moreover, various aspects of complexity create difficulties for complexity assessment. The objective of this paper is to investigate the relationships of various aspects of code complexity and propose a method for identifying the most complex functions. We have conducted an action research project in two software development companies and complemented it with a study of three open source products. Four complexity metrics are measured, and their nature and mutual influence are investigated. The results and possible explanations are discussed with software engineers in industry. The results show that there are two distinguishable aspects of complexity of source code functions: Internal and outbound complexities. Those have an inverse relationship. Moreover, the product of them does not seem to be greater than a certain limit, regardless of software size. We present a method that permits identification of most complex functions considering the two aspects of complexities. The evaluation shows that the use of the method is effective in industry: It enables identification of 0.5% most complex functions out of thousands of functions for reengineering.
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| 6. |
- Ochodek, M., et al.
(författare)
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Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names
- 2020
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Ingår i: Information and Software Technology. - : Elsevier BV. - 0950-5849. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- Context: COSMIC is a widely used functional size measurement (FSM) method that supports software development effort estimation. The FSM methods measure functional product size based on functional requirements. Unfortunately, when the description of the product's functionality is often abstract or incomplete, the size of the product can only be approximated since the object to be measured is not yet fully described. Also, the measurement performed by human-experts can be time-consuming, therefore, it is worth considering automating it. Objective: Our objective is to design a new prediction model capable of approximating COSMIC-size of use cases based only on their names that is easier to train and more accurate than existing techniques. Method: Several neural-network architectures are investigated to build a COSMIC size approximation model. The accuracy of models is evaluated in a simulation study on the dataset of 437 use cases from 27 software development projects in the Management Information Systems (MIS) domain. The accuracy of the models is compared with the Average Use-Case approximation (AUC), and two recently proposed two-step models-Average Use-Case Goal-aware Approximation (AUCG) and Bayesian Network Use-Case Goal AproxImatioN (BN-UCGAIN). Results: The best prediction accuracy was obtained for a convolutional neural network using a word-embedding model trained on Wikipedia+Gigaworld. The accuracy of the model outperformed the baseline AUC model by ca. 20%, and the two-step models by ca. 5-7%. In the worst case, the improvement in the prediction accuracy is visible after estimating 10 use cases. Conclusions: The proposed deep learning model can be used to automatically approximate COSMIC size of software applications for which the requirements are documented in the form of use cases (or at least in the form of use-case names). The advantage of the model is that it does not require collecting historical data other than COSMIC size and names of use cases.
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| 7. |
- Ochodek, M., et al.
(författare)
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Simsax: A measure of project similarity based on symbolic approximation method and software defect inflow
- 2019
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Ingår i: Information and Software Technology. - : Elsevier BV. - 0950-5849. ; 115, s. 131-147
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Tidskriftsartikel (refereegranskat)abstract
- Background: Profiling software development projects, in order to compare them, find similar sub-projects or sets of activities, helps to monitor changes in software processes. Since we lack objective measures for profiling or hashing, researchers often fall back on manual assessments. Objective: The goal of our study is to define an objective and intuitive measure of similarity between software development projects based on software defect-inflow profiles. Method: We defined a measure of project similarity called SimSAX which is based on segmentation of defect-inflow profiles, coding them into strings (sequences of symbols) and comparing these strings to find so-called motifs. We use simulations to find and calibrate the parameters of the measure. The objects in the simulations are two different large industry projects for which we know the similarity a priori, based on the input from industry experts. Finally, we apply the measure to find similarities between five industrial and six open source projects. Results: Our results show that the measure provides the most accurate simulated results when the compared motifs are long (32 or more weeks) and we use an alphabet of 5 or more symbols. The measure provides the possibility to calibrate for each industrial case, thus allowing to optimize the method for finding specific patterns in project similarity. Conclusions: We conclude that our proposed measure provides a good approximation for project similarity. The industrial evaluation showed that it can provide a good starting point for finding similar periods in software development projects.
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| 8. |
- Rana, Rakesh, et al.
(författare)
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Selecting software reliability growth models and improving their predictive accuracy using historical projects data
- 2014
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Ingår i: Journal of Systems and Software. - : Elsevier BV. - 0164-1212 .- 1873-1228. ; 98, s. 59-78
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Tidskriftsartikel (refereegranskat)abstract
- During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for release. SRGMs (software reliability growth models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the model's ability to accurately predict the expected defect inflow profile is useful. For assessing release readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain. Using defect inflow profiles from large software projects from Ericsson, Volvo Car Corporation and Saab, we evaluate commonly used SRGMs for their ability to provide empirical basis for making these decisions. We also demonstrate that using defect intensity growth rate from earlier projects increases the accuracy of the predictions. Our results show that Logistic and Gompertz models are the most accurate models; we further observe that classifying a given project based on its expected shape of defect inflow help to select the most appropriate model. (C) 2014 Elsevier Inc. All rights reserved.
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| 9. |
- Penzenstadler, Birgit, 1981, et al.
(författare)
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AI, Tech, Energy, and Collaboration
- 2023
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Ingår i: IEEE Software. - 1937-4194 .- 0740-7459. ; 40:3, s. 80-83
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Tidskriftsartikel (refereegranskat)abstract
- This edition of the “Practitioner’s Digest” features recent papers on artificial intelligence (AI) and machine learning (ML), along with papers on tech debt, energy consumption, and collaboration between industry and academia.
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| 10. |
- Staron, Miroslaw, 1977, et al.
(författare)
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Improving Quality of Code Review Datasets – Token-Based Feature Extraction Method
- 2021
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Ingår i: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 404, s. 81-93
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Konferensbidrag (refereegranskat)abstract
- Machine learning is used increasingly frequent in software engineering to automate tasks and improve the speed and quality of software products. One of the areas where machine learning starts to be used is the analysis of software code. The goal of this paper is to evaluate a new method for creating machine learning feature vectors, based on the content of a line of code. We designed a new feature extraction algorithm and evaluated it in an industrial case study. Our results show that using the new feature extraction technique improves the overall performance in terms of MCC (Matthews Correlation Coefficient) by 0.39 – from 0.31 to 0.70, while reducing the precision by 0.05. The implications of this is that we can improve overall prediction accuracy for both true positives and true negatives significantly. This increases the trust in the predictions by the practitioners and contributes to its deeper adoption in practice.
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